What is Predictive Analytics?

In today’s business world predicting the future is vital in terms of existence and growth and analytics gives such a platform in terms of a prediction for these businesses.

Predictive analytics is the branch of the analytics which is used to make predictions about unknown future events. This helps with extracting information from data and using it to predict trends and behavior patterns.

There are some steps that are involved in prediction:

  1. Define Project: In this, we define the project outcomes by identifying the datasets that are going to be used.
  2. Data Collection: Data is collected from multiple sources for analysis which provides a complete view of customer interactions.
  3. Data Analysis: This is a process of inspecting, cleaning, modeling data with the objective of discovering useful information to arrive at conclusion.
  4. Statistics: This step is used to analyze the assumptions and test them using standard statistical models.
  5. Modeling: This model has the ability to create accurate predictive models about the future.
  6. Deployment: A Deployment is an option used in prediction which is provided to deploy the results into the everyday decision-making process to get results and output.
  7. Model monitoring: model monitoring and managing are used to ensure that the results are as expected.

Though the predictive analysis is for decades, it is a technology which is used by more organizations nowadays to increase their revenue and competitive advantage. Organizations do use predictive analytics because of these following reasons:

  • Generates more interest in using data which helps to produce more insights.
  • Faster, cheaper
  • Software which is available is easier to use
  • Tougher economic conditions.
  • Need for competitive differentiation.

An organization uses predictive analytics technology to solve the difficult problem and develop new opportunities.

Below are the listed some of the techniques

Detecting fraud: There are a large set of multiple analytics methods used to improve pattern detection and criminal behavior. Cybersecurity which is becoming a growing concern nowadays uses this technique in order to spot out the abnormalities that may indicate the fraud and advanced persistent threats.

Optimizing marketing campaigns: Predictive analytics are used in optimizing market for identifying the customer responses which helps businesses attract, retain and grow their customers.

Improving operations: Many organizations use predictive analytics techniques for forecasting inventory and manage resources. Some of the companies that use prediction are: Airlines use predictive analytics to set ticket prices and hotels uses this technique to estimate the occupancy and increase their profit margin.

Reducing risk: Predictive analytics reduce the risk-related concerns in the organization including insurance claims and collections.

Any organization uses predictive analytics to reduce their risks, optimize their operation and increase its revenue. Here are a few examples where we use it:

  • Banking and Financial Services
  • Retail
  • Oil, Gas & Utilities
  • Governments and Public Sector
  • Health Insurance
  • Manufacturing

When it comes to management, planning, and decision making in an organization, extracting information or, predicting the future values from the available datasets can be an essential business tool. In an organization, predictive models are used to test the existing data for better understanding of the customers and products which increase future opportunities for expansion, growth and reduce risks.

Modeling techniques:

Predictive analytics uses known results to develop a model that can be used to predict values for different or new data. The most widely used predictive modeling techniques are listed below.

Regression model: Regression analysis estimates relationships among variables and is one of the most popular models in statistics. Regression finds the pattern in a large set of datasets and is used to determine some specific patterns such as price, influence in the movement of assets. Linear regression is used where there is only one independent variable which needs to predict the outcome such as Y but in multiple regression, we use two or more independent variables to predict the outcomes.

Neural networks: Neural networks are based on pattern recognition and some AI processes that graphically model parameters. These networks are efficient when no mathematical formula is known that relates inputs to outputs. The ability of these neural networks is to handle nonlinear relationships in data.

Decision trees: Decision trees are classification models that divide the data into subsets based on categories of input variables. A decision tree consists of branches and leaves same as of a tree, where each branch represents a choice to choose in between a number of alternatives that are available in the data. Each leaf represents a classification or decision. This model is used to find the variable in the data which splits the data into logical groups. Decision trees can be easily understood and interpret. They can also handle missing values which are useful for preliminary variable selection. This technique is mostly used when there is a large set of missing data.

Ensemble models: These are the models which are produced by training several similar models. After the training process is done we combine the results to improve accuracy, reduce bias, reduce variance. Ensemble model is mainly used to identify the best model to use with new data.

K-nearest neighbor(knn): This technique is a nonparametric method used for classification and regression. This predicts an object’s values based on the k-closest training examples.

Time series data mining: Time series data is a done at a particular period of time interval such as sales in a month, calls per day, web visits per hour, etc. Time series data mining is used to combine the traditional data mining and forecasting techniques. The other data mining techniques such as sampling, clustering, and decision trees are applied to data collected over a period of time.

Support vector machine: This is mainly used in classification and regression. Support vector machine is a supervised machine learning technique used for learning algorithms which helps us to analyze data and recognize patterns.

Benefits of Predictive Analytics

Predictive analysis in decision-making, optimization, and a predictive search can be applied to a range of business strategies. This is also used in search advertising and recommendation engines. The techniques that are available in predictive analysis helps the managers in the decision-making process to influence sales and revenue forecasting, optimizing the market which further help to optimize manufacturing, and which may even lead to new product development.

 Limitations of predictive analytics

The data which is taken for prediction could be incomplete so the missing values while predicted might result in an error.

The data which is collected from different sources for prediction can vary in quality and format.

Requires more care and attention while processing the data.

Other Applications:

  • Analytical customer relationship management.
  • Child protection.
  • Clinical decision support systems.
  • Collection analysis
  • Customer retention
  • Direct marketing
  • Fraud detection

Leave a Reply

Your email address will not be published.